Reliability Level List Based Iterative SISO Decoding Algorithm for Block Turbo Codes

TELKOMNIKA, Vol.16, No.5, October 2018, pp.2040~2047
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018
DOI: 10.12928/TELKOMNIKA.v16i5.7463  2040
Received October 23, 2017; Revised April 11, 2018; Accepted September 6, 2018
Reliability Level List Based Iterative SISO Decoding
Algorithm for Block Turbo Codes
V Sudharsan, V Vijay Karthik, B Yamuna*
Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore,
Amrita Vishwa Vidyapeetham, India
*Corresponding author, e-mail: b_yamuna@cb.amrita.edu
Abstract
An iterative Reliability Level List (RLL) based soft-input soft-output (SISO) decoding algorithm
has been proposed for Block Turbo Codes (BTCs). The algorithm ingeniously adapts the RLL based
decoding algorithm for the constituent block codes, which is a soft-input hard-output algorithm. The
extrinsic information is calculated using the reliability of these hard-output decisions and is passed as soft-
input to the iterative turbo decoding process. RLL based decoding of constituent codes estimate the
optimal transmitted codeword through a directed minimal search. The proposed RLL based decoder for the
constituent code replaces the Chase-2 based constituent decoder in the conventional SISO scheme.
Simulation results show that the proposed algorithm has a clear advantage of performance improvement
over conventional Chase-2 based SISO decoding scheme with reduced decoding latency at lower noise
levels.
Keywords: error control coding, block turbo code, Iterative SISO decoding, soft decision decoding,
reliability level list
Copyright © 2018 Universitas Ahmad Dahlan. All rights reserved.
1. Introduction
In communication systems, error control coding techniques are used for reliable and
efficient transmission of digital data over noisy channels. P Elias introduced a recursive
approach and constructed concatenated codes by combining an inner code with an outer code
and achieved exponentially decreasing error performance [1]. Tanner proposed a hard in hard
out (HIHO) iterative decoding method for concatenated product block codes and these ‘high
code rate’ concatenated codes found widespread applications in deep space communication
systems [2]. Berrou developed a class of Forward Error Correction (FEC) codes called Turbo
codes constructed by either serial or parallel concatenation of convolutional codes as
constituent codes [3]. These Concatenated Block Codes or Block Turbo Codes (BTCs)
proposed by Elias and Convolutional Turbo Codes (CTC) proposed by Berrou revolutionized the
field of coding theory since they achieved reliable transmission at a code rate closer to Shannon
limit. Since then much research has been done on the decoding algorithms for Turbo codes.
Turbo codes are widely used in 3G/4G mobile communication systems, return channel of
satellite communication systems and IEEE 802.16 (WiMAX). Decoding algorithms for Turbo
codes remains a sought after topic of research in the field of error control coding.
Berrou proposed a soft-input soft-output (SISO) approach for CTC decoding which had
a better performance when compared to HIHO decoding algorithm for BTC proposed by Tanner.
Ramesh Pyndiah proposed a SISO iterative decoding algorithm for BTC using Chase-2
algorithm [4-5]. It was established that SISO decoding approach to BTC could help achieve a
performance similar to CTC with an added advantage of the constituent block decoding not
requiring memory. Dave et. al. proposed a decoding algorithm based on Kaneko’s algorithm for
BTC [6]. Argon et. al. and Al-Dweik et. al. improved the iterative SISO decoding proposed by
Pyndiah to reduce the complexity without compromising much on the performance [7-8].
Belkasmi et. al. have proposed a Genetic Algorithm (GA) based iterative decoding for BTC
where the decoding of constituent block codes has been approached as an optimization
problem [9]. Yuan et. al. had also attempted a GA based decoding for Block Turbo Codes [10].
Askali et. al. have proposed an iterative soft permutation based decoding of product codes [11].
Lu et. al. proposed a syndrome based decoding of BTC and the reduction in complexity of the

TELKOMNIKA ISSN: 1693-6930 
Reliability Level List Based Iterative SISO Decoding.... (V Sudharsan)
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same was put forth by Li et. al. [12-13]. The various novel decoding algorithms for BTC
proposed in the literature target performance improvement or complexity reduction, trading off
one for the other [14-16].
In conventional decoding of BTC, Chase-2 is used as the constituent decoder. The
computational complexity of Chase-2 remains the same even at a higher SNR. To overcome the
complexity of Chase-2 based SISO decoder at lower noise levels, in this paper, a RLL based
decoding algorithm has been proposed for decoding of BTC. Here the constituent codes are
decoded using the RLL based decoding algorithm [17]. The proposed algorithm is found to have
a better coding gain at lower SNR and has a lower decoding latency at higher SNR when
compared to iterative SISO decoding algorithm proposed by Pyndiah, which has been the basis
for most of the recent work in the decoding of BTC. The paper is organized as follows: Section 2
discusses about BTC and the traditional SISO iterative decoding. The proposed RLL based
decoding algorithm for BTC is explained in detail in Section 3. In Section 4, the simulation
results obtained using the proposed algorithm are discussed, followed by conclusion
in Section 5.
2. SISO Decoding of Block Turbo Codes
Block Turbo Codes are a class of FEC codes constructed by serial concatenation of
Block codes like BCH and RS codes. Consider two systematic BCH codes C1 (n1, k1, d1) and C2
(n2, k2, d2) where ‘n’ is the codeword length, ‘k’ is the message length and ‘d’ is the minimum
hamming distance. The Block Turbo Code is constructed by encoding the 2-D message block
with an encoding scheme C1. The encoded block is passed to an interleaver and the interleaved
code block is subsequently encoded using C2-the second encoding scheme. Thus, for a
message block of dimension k1 x k2, the turbo code T=C1  C2 can be obtained by:
a. Encoding k1 rows using the coding scheme C2.
b. Encoding k2 columns using the coding scheme C1.
The resultant BTC can be represented as where , ,
. Consider a 2-D message block of bits {0,1} encoded into a codeword block using
encoding scheme ‘T’, modulated using BPSK and transmitted over an Additive White Gaussian
Noise (AWGN) channel with mean zero and variance No/2. The received soft value matrix from
the demodulator is given as in equation (1) below:
(1)
where R = {r11,r12,…r1n1; r21,r22,...;….rn1n2}. The turbo decoding of BTC involves two main steps
namely decoding of constituent Block Code and iterative turbo decoding process.
2.1. Decoding of constituent Block Code
Each row or column in the 2-D received matrix is decoded using the corresponding
elementary decoder. The traditional SISO decoding proposed by Pyndiah uses Chase-2
algorithm. The Chase-2 algorithm [5] involves the following steps:
a. Identify the ⌊ ⌋ least reliable positions (LRPs) using the received soft decision
sequence.
b. Generate 2
p
error patterns ‘ei’ at the LRPs and obtain 2
p
distorted sequences ‘Zi’ using the
hard decision sequence ‘y’.
(2)
c. Decode each of the 2
p
distorted sequences using an algebraic decoder or a hard decision
decoder and add the decoded codeword to the candidate set Ω.
d. Euclidean distance of each candidate codeword from the original received soft decision
sequence is calculated and the codeword with closest Euclidean metric is taken as the
decision codeword D.
2.2. Iterative SISO Turbo decoding
In the iterative decoding process, the decoding of the received soft decision matrix is
done over a fixed number of iterations. Each iteration consists of two half iterations namely row

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decoding and column decoding. An extrinsic information is calculated based on the decision D
from the elementary Chase decoder and is passed from one half iteration to the consecutive
half iteration. The iterative process follows the steps as given below:
a. The reliability values of the received soft decision sequence is defined in terms of its Log
Likelihood Ratio (LLR) as given by equation (3).
⋀ ( ) ( ) . (3)
b. In each half iteration, each row/column in the received matrix is passed to the elementary
constituent decoder and decision D is obtained. Decision D {0,1} is mapped to {-1, +1}.
c. The soft-output of the current half iteration is computed as in equation (4).
(
| | | |
) (4)
Here ‘C’ is the competing codeword in the set Ω which has the second least Euclidean distance
to R. In certain cases, the complexity of finding the competing codeword increases exponentially
with respect to ‘p’. In such cases, soft-output is calculated as in equation (5) given below.
(5)
where ‘β’ is a reliability scaling factor.
d. The extrinsic information for the next half iteration is obtained as in equation (6).
(6)
e. The extrinsic information is then added to the soft-input of the next half iteration as in
equation (7).
(7)
where ‘α’ is the weighting factor that controls the effect of extrinsic information at the earlier
iterations when BER is high. The process is now repeated for the subsequent iterations until the
predefined number of iterations is reached. The output from the final iteration is taken as the
optimal estimate of transmitted code block.
3. The proposed RLL Based Iterative Decoding Algorithm for Block Turbo Codes
The main components of BTC based decoding scheme are an elementary decoder for
each of the constituent block code and iterative SISO turbo decoding, where exchange of
extrinsic information between iterations take place. There has been much work in the literature
on complexity reduction in iterative SISO decoding and extrinsic information calculations, with
Chase-2 algorithm as the decoder for constituent codes. The fact that the computational
complexity of Chase-2 decoding algorithm remains the same even at a lower noise level has
lead researchers to come up with modifications/alternate solutions to Chase-2. One such
algorithm is Reliability Level List based SDD Algorithm [14].
RLL based decoding is a scheme of soft decision decoding based on an efficient and
meaningful strategy of exploiting the reliability information available from the demodulator. The
algorithm is based on the RLL which forms the framework for identifying the codeword through a
directed minimal search. Using this framework of RLL, we propose an iterative algorithm for
decoding BTC. For each row/column decoding, the RLL gives the most reliable valid codeword
which forms the decision D. The reliability of the decision D is then calculated using
equation (4), where the competing codeword ‘C’ is the second most reliable valid codeword in
the RLL. This soft-output of the current iteration is passed as soft-input to the next half iteration.
The proposed algorithm gives a better performance at lower SNR and lower decoding latency at
higher SNR when compared to iterative Chase-2 based SISO decoding algorithm for BTC.

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The two main steps of the proposed algorithm are RLL generation to find decision D, followed
by iterative SISO decoding.
3.1. RLL generation for decoding constituent Block Code
Based on the reliability values of the ‘n’ bit soft decision sequence, a rank ‘v’ is assigned
to all possible codewords of the constituent block code in a structured manner. This is called
generation of RLL. Each entry in the RLL generated is given by a set of parameters (vi,pi,qi,mi)
where
i=0 to 2
n-1
.
v-rank of the entry.
p-set of indices which form the least reliable bit positions.
q-codeword obtained by flipping the hard decision sequence at positions given by pi.
m-absolute sum of reliability values at positions given by pi.
The procedure for RLL generation is as follows:
a. By default, first 3 entries of the RLL are hard decision sequence (q0, v=1, p0={ }, m=0),
codewords obtained by flipping the least reliable position(q1, v=2, p1={‘1’}, m=rj; j-1
st
LRP)
and second least reliable position(q2, v=3, p1={‘2’}, m=rj; j-2
nd
LRP) respectively in the hard
decision sequence.
b. A pending list ‘P’ is formed which contains all possible contestants for the next entry to the
list.
c. Further the pending list is updated based on the current entry ‘vi’ under consideration.
Updation of pending list is done as follows:
i. If the least index in ‘pi’ is not ‘1’, add ‘1’ to the index set and add it to the pending list.
ii. For all non-zero indices in the set ‘pi’, add 1 to the index and check if it is less than the
next non-zero element in the index set ‘q’ and add the index set to the pending list.
iii. For the last non-zero index in the set ‘pi’, add 1 to it and check if it is less than the
codeword length ‘n’ and add the entry to the pending list.
iv. If the index set generated by any of the above rules is already a member in the pending
list, remove it from the list.
d. The entry ‘qi’ with least value of ‘m’ from the pending list forms the next entry to the RLL.
Once a member from the pending list is chosen to be the next entry, it is checked if it is a
valid codeword.
e. If the current entry ‘qi’ is a valid codeword, then the decoding is terminated and ‘qi’ is taken
as the decision D. Else the pending list is updated based on the current entry ‘pi’ and the
process is repeated until a valid codeword from RLL constituent decoder is obtained.
3.2. RLL based iterative SISO decoder for BTC
With D as the output of the RLL constituent decoder, the process of RLL search is
continued to find the next most probable codeword which is the competing codeword C. This is
the second most reliable valid codeword in the RLL. The soft-output is then calculated as a
function of the competing codeword C as in equation (4) which is then passed as extrinsic
information to the next constituent decoder. The soft-input for the next half iteration is then
calculated as in equation (6) and equation (7). The process is repeated for the fixed number of
iterations and decision ‘D’ of the final iteration gives the optimal estimate.
In Chase decoders, the complexity of finding the competing codeword increases if the
competing codeword is not in the candidate codeword set Ω. This is because the scanning
radius has to be extended beyond (dmin/2), resulting in more candidate codewords in the set Ω,
with a corresponding increase in number of HDD. In the proposed algorithm, since HDD is not
involved, the complexity associated with the same is avoided.
The proposed RLL based iterative SISO decoding algorithm for BTC is given below:
1. The reliability value R of the matrix is defined in terms of it’s LLR value as
⋀ ( ) ( ) .
2. For each row in R:
Do
2.1. Index the bit positions in increasing order of magnitudes from 1 to n.

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2.2. Define pending list with parameters (vi, pi, qi, mi) where i = 0 to 2
n-1
.
2.3. Include the hard decision sequence (q0, v=1, p0 = {}, m=0), codewords obtained by
flipping the least reliable position (q1, v=2, p1={‘1’}, m=r1) and second reliable
position(q2, v=3, p1={‘2’}, m=r2) of the hard decision sequence as the first 3 entries to
the pending list.
2.4. For each entry (vi,pi,qi,mi) in RLL:
2.4.1. Do
If (qi is a valid codeword) :
qi = decoded codeword
jump to 3
Else: Remove the current ‘pi’ from RLL and Update the pending list
If (‘1’ ∉ pi) :
add ‘1’ to indices set
add the new entry to pending list
End
For each non-zero element ‘bj’ in pi:
If ((bj+1) < bj+1 && (bj+1) ≤ n):
add (bj+1) to indices set
add the new entry to pending list
End
End
End
2.4.2. For each newly added candidate in the pending list, calculate ‘mi’ by adding the
soft values in the indices mentioned in ‘pi’.
2.4.3. Choose the candidate with least value of ‘mi’ as next entry to the RLL.
3. Map the decoded codeword qi {0,1} to D {-1, +1}.
4. Calculate the soft-output of the decoder using the formula
(
| | | |
)
where ‘C’ is the competing codeword. In practical cases, where the complexity of finding ‘C’
increases, soft-output is calculated as
where β is reliability scaling factor [5].
5. Calculate the extrinsic information as
6. Calculate soft-input to the next half iteration as
where α is weighting factor.
7. Repeat Steps 2-6 for the column decoding.
8. Repeat Steps 2-7 for each iteration until we reach the fixed number of iterations.
The Decision ‘D’ of the final half-iteration gives the optimal estimate of the transmitted BTC.
4. Results and Discussion
The performance of the proposed algorithm is evaluated in an AWGN channel under
BPSK modulation. The results obtained using this algorithm for BTC (15,7,5)
2
, BTC (31,21,5)
2
and BTC (31,21,5)x(31,26,3) are compared against the traditional iterative SISO decoding
proposed by Pyndiah. All simulations have been carried out using Matlab. A 4-iteration
simulation has been shown for all the codes considered. The weighting factor α is taken as α(m)

2045
= [0 0.2 0.4 0.5 0.7 0.9 1.0 1.0] and reliability scaling factor is taken to be β(m) = [0.2 0.4 0.6 0.8
1.0 1.0 1.0 1.0] [5]. The weighting and scaling factors are chosen to increase with the iteration
number as the decision D obtained from a particular iteration is more reliable than the previous
iteration.
In Figure 1, the improvement of the proposed algorithm over iterations is compared
against conventional SISO decoding algorithm for a BTC (15,7,5)
2
. At a BER of 2x10
-4
, the
proposed RLL based SISO decoding algorithm is found to have a coding gain of 1 dB over the
Chase-2 based conventional SISO decoding algorithm proposed by Pyndiah using Chase-2
algorithm (Ch-Py). With the proposed algorithm, the decoding latency reduces at higher SNR.
The decoding latency of RLL-SISO decoding algorithm and Chase-2 based conventional SISO
at SNR 1 dB and SNR 5 dB are shown in Table 1.
Figure 1. Performance comparison of RLL-SISO decoding algorithm with Conventional SISO for
BTC (15,7,5)
2
Table 1. Decoding latency of the proposed algorithm for decoding one frame of BTC (15,7,5)
2
against conventional SISO algorithm
Decoding algorithm SNR = 1 dB SNR = 5 dB
RLL SISO 4.6924 sec 0.0971 sec
Conventional SISO 1.07 sec 1.09 sec
The performance comparison of the proposed algorithm for BTC (31,21,5)
2
has been
shown in Figure 2. It can be seen that RLL based SISO decoding algorithm has a coding gain of
0.7 dB over conventional algorithm at the end of iteration 4. The proposed algorithm works well
for asymmetric codes as well and it has been applied to an asymmetric BTC (31,21,5)x
(31,26,3) and the improvement over iterations has been shown in Figure 3.
In traditional SDD algorithms, the search space is generally bounded to a limit. But the
RLL algorithm is not a bounded search algorithm and the search space is the entire 2
n
codewords. The algorithm is applicable to BTCs of any dimension and adapts well for mobile
applications where the coding scheme changes often. Most of the SDD algorithms in literature
do not make use of the high-SNR environment in which transmission takes place and have a
fixed computations at all SNRs. RLL based iterative algorithm has a faster decoding
convergence for high-SNR applications where it is likely to find the optimal codeword in the first
few entries itself thus reducing the complexity drastically.

 ISSN: 1693-6930
2046
Figure 2. Performance of RLL-SISO algorithm for BTC (31,21,5)
2
at 4
th
iteration
Figure 3. Performance improvement of RLL-SISO algorithm over iterations for BTC
(31,21,5) x (31,26,3)
5. Conclusion
The proposed algorithm gives a better performance for BTC decoding and has very low
complexity at high SNR applications. This algorithm makes maximum use of the channel
reliability information and does a structured search over the entire 2
n
codeword space thus
giving the most reliable codeword as the decoded codeword. The algorithm is generalized for
any Turbo code constructed using cyclic block codes as constituent codes. Much of the SISO
decoders for BTC revolve around LRP based Chase-2 decoders and thus the proposed RLL
algorithm widens the scope for research in alternate SISO decoders for BTC. Further the
algorithm can extended to BTC with non-binary cyclic codes as constituent codes [18].
References
[1] Elias P. Error-free Coding. Transactions of the IRE Professional Group on Information Theory. 1954;
4(4): 29-37.
[2] Tanner R. A recursive approach to low complexity codes. IEEE Transactions on Information Theory.
1981; 27(5): 533-547.

2047
[3] Berrou C, Glavieux A, Thitimajshima P. Near Shannon limit error-correcting coding and decoding:
Turbo-codes. IEEE Int Conf Communications. 1993; 23: 1064-1071.
[4] Pyndiah R. Near-optimum decoding of product codes: block turbo codes. IEEE Transactions on
Communications. 1998; 46(8): 1003-1010.
[5] Chase D. Class of algorithms for decoding block codes with channel measurement information. IEEE
Transactions on Information Theory. 1972; 18(1): 170-182.
[6] Dave S, Junghwan Kim, Kwatra S. An efficient decoding algorithm for block turbo codes. IEEE
Transactions on Communications. 2001; 49(1): 41-46.
[7] Argon C, McLaughlin S. An Efficient Chase Decoder for Turbo Product Codes. IEEE Transactions on
Communications. 2004; 52(6): 896-898.
[8] Al-Dweik A, Goff S, Sharif B. A Hybrid Decoder for Block Turbo Codes. IEEE Transactions on
Communications. 2009; 57(5): 1229-1232.
[9] Belkasmi M, Berbia H, Bouanani F. 7th International ITG Conference on Source and Channel Coding
(SCC). 2008: 1-6.
[10] Y Yuan J, Tian Y, Hu X, Huang S, Lin J, Pang Y. A novel BTC decoding algorithm based on the
genetic algorithm in optical communication systems. Optoelectronics Letters. 2014; 10(2): 123-125.
[11] Askali M, Ayoub F, Chana I, Belkasmi M. Iterative Soft Permutation Decoding of Product Codes.
Computer and Information Science. 2016; 9(1): 128.
[12] Lu E, Lu P. A syndrome-based hybrid decoder for turbo product codes. Tainan: International
Symposium on Computer Communication Control and Automation (3CA). 2010: 280-282.
[13] Li L, Zhang F, Zheng P, Yang Z. Improvements for decoding algorithm of turbo product code. IEEE
International Conference on Signal Processing, Communications and Computing (ICSPCC). 2014:
374-378.
[14] Kumar M, Saxena J. Performance Comparison of Latency for RSC-RSC and RS-RSC Concatenated
Codes. Indonesian Journal of Electrical Engineering and Informatics (IJEEI). 2013; 1(3).
[15] Salim M, Yadav R, Narwal K, Sharma A. A New Block S-Random Interleaver for Shorter Length
Frames for Turbo Codes. Bulletin of Electrical Engineering and Informatics. 2013; 2(4).
[16] Wang J, Li J, Cai C. Two Novel Decoding Algorithms for Turbo Codes Based on Taylor Series in
3GPP LTE System. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2014; 12(5).
[17] Yamuna B, Padmanabhan TR. A Reliability Level List based SDD Algorithm for Binary Cyclic Block
Codes. International Journal of Computers Communications & Control. 2012; 7(2): 388-395.
[18] Yamuna B, Padmanabhan TR. A minimal search soft decision list decoding algorithm for Reed-
Solomon codes. International Journal of Information and Communication Technology. 2014; 6(1): 71-
85.

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